log2 fold change (MLE): cell line P1E10 vs Parental
Wald test p-value: cell line P1E10 vs Parental
DataFrame with 56752 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue
<numeric> <numeric> <numeric> <numeric> <numeric>
GAS5 3358381 0.1558718 0.0336216 4.636061 3.55110e-06
SNORD30 1605167 0.0910242 0.0577513 1.576141 1.14993e-01
SNORD49A 1156164 0.1842127 0.0437292 4.212578 2.52473e-05
SNORD26 857547 0.0954226 0.0540887 1.764186 7.77006e-02
SNHG1 780897 -0.0217632 0.0401173 -0.542488 5.87482e-01
... ... ... ... ... ...
PJY186_RUNX1:total 0 NA NA NA NA
PJY187_VEGFA:total 0 NA NA NA NA
PJY300_mDNMT1:total 192298 0.0560998 0.0433756 1.29335 0.195891
PJY302_EMX1:total 0 NA NA NA NA
PJY306_EMX1:total 0 NA NA NA NA
padj
<numeric>
GAS5 8.06429e-05
SNORD30 4.52981e-01
SNORD49A 4.94390e-04
SNORD26 3.62830e-01
SNHG1 8.79975e-01
... ...
PJY186_RUNX1:total NA
PJY187_VEGFA:total NA
PJY300_mDNMT1:total 0.588356
PJY302_EMX1:total NA
PJY306_EMX1:total NA
Code
summary(res_batch2_day1)
out of 51625 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 937, 1.8%
LFC < 0 (down) : 1751, 3.4%
outliers [1] : 1, 0.0019%
low counts [2] : 23896, 46%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
log2 fold change (MLE): cell line P1E10 vs Parental
Wald test p-value: cell line P1E10 vs Parental
DataFrame with 56752 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue
<numeric> <numeric> <numeric> <numeric> <numeric>
GAS5 3192415 0.18567762 0.0303354 6.120823 9.30933e-10
SNORD30 1514330 0.15582664 0.0512274 3.041861 2.35120e-03
SNORD49A 1170290 0.19302856 0.0428252 4.507358 6.56398e-06
SNORD26 825436 0.09512922 0.0398426 2.387627 1.69575e-02
SNHG1 678111 0.00437696 0.0311625 0.140456 8.88300e-01
... ... ... ... ... ...
PJY186_RUNX1:total 0 NA NA NA NA
PJY187_VEGFA:total 0 NA NA NA NA
PJY300_mDNMT1:total 129466 0.402798 0.0695254 5.79354 6.89182e-09
PJY302_EMX1:total 0 NA NA NA NA
PJY306_EMX1:total 0 NA NA NA NA
padj
<numeric>
GAS5 3.28203e-08
SNORD30 2.70614e-02
SNORD49A 1.37614e-04
SNORD26 1.31104e-01
SNHG1 9.76136e-01
... ...
PJY186_RUNX1:total NA
PJY187_VEGFA:total NA
PJY300_mDNMT1:total 2.20407e-07
PJY302_EMX1:total NA
PJY306_EMX1:total NA
Code
summary(res_batch2_day2)
out of 50458 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 880, 1.7%
LFC < 0 (down) : 1599, 3.2%
outliers [1] : 53, 0.11%
low counts [2] : 25268, 50%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895